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Prueba de causalidad de Granger×Causalidad de Granger con Panel Bootstrap de Kónya×Modelo de Efectos Fijos para Datos de Panel×
CampoEconometríaEconometríaEconometría
FamiliaRegression modelHypothesis testRegression model
Año de origen196920062014
Autor originalClive W. J. GrangerLászló KónyaHsiao (textbook treatment); within transformation of panel data
TipoTime-series predictive causality testNon-parametric bootstrap hypothesis testPanel data regression
Fuente seminalGranger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992. DOI ↗Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗
AliasGranger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik TestiBootstrap Panel Causality Test, Kónya Panel Granger Causality, SUR-Based Bootstrap Causality, Kónya Önyükleme Nedensellik Testifixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Relacionados535
ResumenThe Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.Introduced by László Kónya in 2006, this method tests Granger causality in heterogeneous panels by estimating a Seemingly Unrelated Regressions (SUR) system and deriving country-specific critical values through bootstrapping. Unlike pooled panel tests, it delivers a separate causality verdict for each cross-section, making it particularly valuable in applied macroeconomics and international economics when panel units are expected to behave differently.The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).
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ScholarGateComparar métodos: Granger Causality · Kónya Bootstrap Causality · Panel Fixed Effects. Recuperado el 2026-06-18 de https://scholargate.app/es/compare